python
import tensorflow as tf
model = tf.keras.models.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(24, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(train_sequences, train_labels, epochs=num_epochs, validation_data=(val_sequences, val_labels))
python
import tensorflow as tf
encoder_inputs = tf.keras.layers.Input(shape=(encoder_sequence_length,))
encoder_embedding = tf.keras.layers.Embedding(encoder_vocab_size, embedding_dim)(encoder_inputs)
encoder_outputs, state_h, state_c = tf.keras.layers.LSTM(latent_dim, return_state=True)(encoder_embedding)
encoder_states = [state_h, state_c]
decoder_inputs = tf.keras.layers.Input(shape=(decoder_sequence_length,))
decoder_embedding = tf.keras.layers.Embedding(decoder_vocab_size, embedding_dim)(decoder_inputs)
decoder_lstm = tf.keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)
decoder_dense = tf.keras.layers.Dense(decoder_vocab_size, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = tf.keras.models.Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit([encoder_sequences, decoder_sequences_in], decoder_sequences_out, epochs=num_epochs, validation_data=([val_encoder_sequences, val_decoder_sequences_in], val_decoder_sequences_out))
python
import tensorflow as tf
import tensorflow_addons as tfa
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Embedding(input_dim=num_words, output_dim=embedding_dim, input_length=max_len))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units=hidden_size, return_sequences=True)))
model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(num_tags, activation='relu')))
crf = tfa.layers.CRF(num_tags)
model.add(crf)
model.compile(optimizer='adam', loss=crf.loss_function, metrics=[crf.accuracy])
model.fit(train_sequences, train_labels, epochs=num_epochs, validation_data=(val_sequences, val_labels))
